Route damage prediction system and method

ABSTRACT

A route damage prediction system includes cameras, a conversion unit, and an analysis unit. The cameras obtain image data that include a route traveled upon by vehicles. The image data includes still images and/or video of the route obtained at different times. The conversion unit includes one or more computer processors configured to at least one of create wireframe model data or modify the image data into the wireframe model data representative of the route. The analysis unit includes one or more computer processors configured to examine changes in the wireframe model data to identify a historical trend of changes in the image data. The analysis unit is configured to compare the historical trend of the changes in the image data with designated patterns of changes in the wireframe model data to determine when to request at least one of repair, inspection, or maintenance of the route.

FIELD

Embodiments of the subject matter disclosed herein relate to examiningroutes traveled by vehicles for predicting damage to the routes.

BACKGROUND

Routes that are traveled by vehicles may become damaged over time withextended use. For example, tracks on which rail vehicles travel maybecome broken, cracked, pitted, misaligned, or the like, over time. Thisdamage can pose threats to the safety of the rail vehicles, thepassengers located thereon, and nearby persons and property. Forexample, the risks of derailment of the rail vehicles can increase whenthe tracks become damaged.

Some known systems and methods that inspect the tracks involve emittingvisible markers on the tracks and optically monitoring these markers todetermine if the tracks have become misaligned. These visible markersmay be created using laser light, for example. But, these systems andmethods can require additional hardware in the form of a light emittingapparatus, such as a laser light source. This additional hardwareincreases the cost and complexity of the systems, and can requirespecialized rail vehicles that are not used for the conveyance ofpassengers or cargo. Additionally, these systems and methods typicallyrequire the rail vehicle to slowly travel over the tracks so that thevisible markers can be examined.

Other known systems and methods involve injecting electric current intothe tracks and examining changes to the current to identify opencircuits caused by breaks in the tracks. But, these systems and methodsalso may require additional hardware to inject the current and to sensethe current, and may be prone to false identifications of damage to theroute.

BRIEF DESCRIPTION

In one example of the inventive subject matter, a system (e.g., a routedamage prediction system) includes one or more cameras, a conversionunit, and an analysis unit. The cameras are configured to obtain imagedata within one or more fields of view of the one or more cameras thatinclude a route that is traveled upon by plural different vehicles. Theimage data includes at least one of still images or video of the routeobtained at different times. The conversion unit includes one or morecomputer processors configured to at least one of create wireframe modeldata or modify the image data into the wireframe model datarepresentative of the route. The analysis unit includes one or morecomputer processors configured to examine changes in the wireframe modeldata to identify a historical trend of changes in the image data. Theanalysis unit is configured to compare the historical trend of thechanges in the image data with designated patterns of changes in thewireframe model data to determine when to request at least one ofrepair, inspection, or maintenance of the route.

In another example of the inventive subject matter described herein, amethod (e.g., for predicting damage to a route) includes receiving imagedata having one or more fields of view that include a route that istraveled upon by plural different vehicles. The image data includes atleast one of still images or video of the route obtained at differenttimes. The method also includes at least one of creating wireframe modeldata or modifying the image data into the wireframe model datarepresentative of the route, examining changes in the wireframe modeldata to identify a historical trend of changes in the image data, andcomparing the historical trend of the changes in the image data withdesignated patterns of changes in the wireframe model data to determinewhen to request at least one of repair, inspection, or maintenance ofthe route.

In another example of the inventive subject matter described herein, asystem (e.g., a route damage prediction system) includes a conversionunit and an analysis unit. The conversion unit is configured to receiveimage data acquired at different times, the image data representing atleast one of images or video of a common segment of a route traveled byvehicles. The conversion unit configured to create wireframe model datafrom the image data. The analysis unit is configured to examine thewireframe model data to identify changes in the wireframe model dataover time. The analysis unit also can be configured to examine thechanges in the wireframe model data to determine when to request atleast one of repair, maintenance, or inspection of the common segment ofthe route.

BRIEF DESCRIPTION OF THE DRAWINGS

Reference is made to the accompanying drawings in which particularembodiments and further benefits of the invention are illustrated asdescribed in more detail in the description below, in which:

FIG. 1 illustrates a route damage prediction system according to oneexample embodiment of the inventive subject matter described herein;

FIG. 2 illustrates an image representative of image data acquired by acamera onboard a vehicle shown in FIG. 1 according to one example of theinventive subject matter described herein;

FIG. 3 illustrates wireframe model data of the image data shown in FIG.2 according to one example of the inventive subject matter describedherein;

FIG. 4 illustrates wireframe model data 404 representative of the imagedata acquired of a route shown in FIG. 1 according to another example ofthe inventive subject matter described herein;

FIG. 5 illustrates additional wireframe model data representative ofimage data acquired of the route shown in FIG. 1 according to anotherexample of the inventive subject matter described herein; and

FIG. 6 illustrates a flow chart of a method for analyzing image dataover time of a route to predict when repair and/or maintenance of theroute may be needed according to an example embodiment of the inventivesubject matter described herein.

DETAILED DESCRIPTION

One or more examples of the inventive subject matter described hereininclude systems and methods for imaging a route traveled by one or morevehicles over time and, based on image data acquired of the route byimaging systems on the one or more vehicle systems, predicting whenrepair or maintenance of the route is needed. For example, a history ofthe image data can be inspected to determine if the route exhibits apattern of degradation over time. Based on this pattern, a services team(e.g., a group of one or more personnel and/or equipment) can identifywhich sections of the route are trending toward a bad condition oralready are in bad condition, and then may proactively perform repairand/or maintenance on those sections of the route to avoid futureaccidents.

In one aspect, cameras mounted on the vehicles are oriented toward theroute being traveled upon to capture image data (e.g., still imagesand/or videos) as the vehicles move on the routes at the same ordifferent times. The cameras can be mounted relatively close to route toobtain high quality image data of the route. The image data can becommunicated from the vehicles to an examination system disposedoff-board the vehicles. Optionally, all or part of the examinationsystem can be disposed onboard one or more of the vehicles. The imagedata can be communicated from the vehicles to the examination systemperiodically, in response to receiving a command or request for theimage data, when the vehicles enter into one or more designatedlocations (e.g., a vehicle yard such as a rail yard), or otherwise. Theexamination system can include one or more computing devices (e.g.,computers, such as remote servers). The image data from multipledifferent vehicles acquired at different times of the same segments ofthe route can be examined to determine changes in the condition of theroute. The image data obtained at different times of the same segmentsof the route can be examined in order to filter out external factors orconditions, such as the impact of precipitation (e.g., rain, snow, ice,or the like) on the appearance of the route, from examination of theroute.

As one example, the examination system can receive image data fromdifferent vehicles, convert the image data into wireframe model data,and examine changes in the wireframe model data over time to predictwhen the route will need maintenance and/or repair. The image data canbe converted into the wireframe model data by identifying pixels orother locations in the image data that are representative of the same orcommon edges, surfaces, or the like, of objects in the image data. Thepixels or other locations in the image data that represent the sameobjects, surfaces, edges, or the like, may be identified by theexamination system by determining which pixels or other locations in theimage data have similar image characteristics and associating thosepixels or other locations having the same or similar imagecharacteristics with each other.

The image characteristics can include the colors, intensities,luminance, locations, or other information of the pixels or locations inthe image data. Those pixels or locations in the image data havingcolors (e.g., wavelengths), intensities, and/or luminance that arewithin a designated range of each other and/or that are within adesignated distance from each other in the image data may be associatedwith each other by the examination system. The examination system cangroup these pixels or locations with each other because the pixels orlocations in the image data likely represent the same object (e.g., arail of a track being traveled by a rail vehicle).

The pixels or other locations that are associated with each other can beused to create a wireframe model of the image data, such as an imagethat represents the associated pixels or locations with lines of thesame or similar colors, and other pixels or location with a differentcolor. The examination system can generate different wireframe models ofthe same segment of a route from different sets of image data acquiredat different times (and/or by imaging systems onboard differentvehicles). The examination system can compare these different wireframemodels and, depending on the differences between the wireframe modelsthat are identified by the examination system, identify and/or predictdamage to the route, and/or when maintenance and/or repair is needed forthe route.

In one aspect, the examination system may have different predictedamounts of damage to the route associated with different changes in thewireframe data. For example, detection of a bend or other misalignmentin the route based on changes in the wireframe model data may beassociated with more damage to the route than other types of changes inthe wireframe model data. As another example, the changing of a solidline in earlier wireframe model data to a segmented line in laterwireframe model data can be associated with different degrees of damageto the route based on the number of segments in the segmented line, thesize of the segments and/or gaps between the segments in the segmentedline, the frequency of the segments and/or gaps, or the like. Based onthe degree of damage identified from changes in the wireframe modeldata, the examination system may automatically order maintenance and/orrepair of the route.

FIG. 1 illustrates a route damage prediction system 100 according to oneexample embodiment of the inventive subject matter described herein. Thesystem 100 includes one or more cameras 102 that are configured toobtain image data within a field of view 104 of the cameras 102. Thefield of view 104 includes a route 106 being traveled by a vehicle 108.As a result, the image data encompasses still images and/or videos ofthe route 106.

The vehicle 108 is described and shown herein as representing a railvehicle, such as a locomotive or other rail vehicle. Optionally, thevehicle 108 may represent another vehicle, such as another off-highwayvehicle. An off-highway vehicle can include a mining vehicle or othervehicle that is not designed or permitted for travel on public roadways.The vehicle 108 alternatively may represent an automobile or other typeof vehicle. The route 106 may represent a track formed of one or morerails, a road, or other type of surface on which vehicle 108 can move.

Several different vehicles 108 may include different cameras 102 thattravel over the same route 106 to obtain image data of the same sectionsof the route 106 at different times. Optionally, one or more cameras 102that obtain image data of the route 106 can be disposed off-board thevehicles 108. For example, one or more of the cameras 102 can be part ofa wayside device that remains stationary with respect to the ground onwhich the route 106 is disposed.

The cameras 102 obtain image data that includes still images and/orvideos of the route 106 at different times. For example, the image datagenerated by the cameras 102 can represent the same parts of the route106 at different hours, days, weeks, months, years, or other timeperiods. The image data can be obtained by the cameras 102 while thevehicles 108 are moving along the route 106. For example, the camerascan obtain the image data while the vehicles 108 are moving at an upperspeed limit associated with the route, such as a track speed of theroute.

The cameras 106 can be operatively connected with a camera controller110. By operatively connected, it is meant that the camera 102 can beconnected with the camera controller 110 by one or more wired and/orwireless connections, such as one or more wires, cables, buses, wirelessnetworks, train lines, multiple unit cables or the like. The cameracontroller 110 represents and/or includes hardware circuitry and/orcircuits, that include and/or are connected with one or more computerprocessors, such as one or more microprocessors or other electroniclogic-based devices. The camera controller 110 controls operations ofthe camera 102, such, as controlling when the camera 102 obtains and/orgenerates image data, the settings of the camera 102 (e.g., focal point,aperture size, resolution, or the like), or other aspects of the camera102. For example, the camera controller 110 can control time periodswhen the camera 102 is ON and obtaining image data, the resolution ofthe camera 102 (such as the number of pixels per unit area of the camera102), the type of image data obtained by the camera 102 (such as whetheror not the camera is obtaining the image data as still images, video, orother types of images).

A vehicle controller 112 of the vehicle 108 includes or representshardware circuitry and/or circuits that include and/or are connectedwith one or more computer processors, such as one or moremicroprocessors or other electronic logic-based devices. The vehiclecontroller 112 controls operations of the vehicle 108. The vehiclecontroller 112 can be used to manually and/or autonomously control thetractive effort and/or breaking effort of the vehicle 108, among otherfunctions, and may include or represent one or more input and/or outputdevices such as throttles, levers, peddles, or the like.

A memory device 114 disposed onboard the vehicle 108 can include orrepresent one or more computer readable storage devices, such as acomputer hard-drive, an optical drive, a flash drive, an electricallyprogrammable read only memory, a random accessible memory, a read onlymemory, or another type of computer readable memory device. The memorydevice 114 can store the image data that is output by the camera 102.Optionally, the memory device 114 may be disposed off-board the vehicle108.

A communication unit 116 disposed onboard the vehicle 108 allows theimage data to be communicated from the vehicle 108. As used herein, theterm “unit” can refer to hardware circuits or circuitry that includeand/or are connected with one or more processors, such as one or morecomputer microprocessors or other computer processors, or otherelectronic logic-based devices. The communication unit 116 can includetransceiving equipment and/or circuitry which may include and/or beconnected with one or more devices that can wirelessly communicateinformation to one or more off-board devices, such as an antenna 118.Additionally or alternatively, the communication unit 116 can includeand/or be connected with transceiving equipment and/or circuitry thatcommunicate signals over one or more wired connections 120, such as acable, bus, wire, train line, multiple unit cable, or the like. Thewired connection 120 can be used to communicate the image data toanother vehicle (e.g., a vehicle that is mechanically coupled with theillustrated vehicle 108 to travel together along the route 106 in avehicle consist) and/or to an off-board location, such as when thevehicle 108 is stationary and the wired connection 120 is connected withanother wired connection to communicate the image data off of thevehicle 108.

The system 100 can include an examination system 122 that receives imagedata of the route 106 obtained by camera 102 on the same or differentvehicles 108. The image data that represents the same segment of theroute 106 can be acquired by cameras on the same or different vehicles108 at different times. The examination system 122 is shown as beingoff-board the vehicle 108, but optionally may be partially or entirelydisposed onboard one or more vehicles 108.

The examination system 122 includes a communication unit 126. Thecommunication unit 126 can be similar to the communication unit 116 thatis onboard the vehicle 108. For example, the communication unit 126 caninclude transceiving equipment and/or hardware, such as an antenna 124,that wirelessly communicates with the communication unit 116 to receivethe image data. Optionally, the communication unit 126 can include oneor more wired connections 125 that can receive the image data from thecommunication unit 116 when the wired connections 112, 125 are directlyor indirectly connected with each other.

The communication unit 126 communicates with the communication unit 116in order to receive the image data obtained by the cameras 102. Thecommunication unit 126 can communicate with several vehicles 108 inorder to obtain and examine the image data obtained and/or generated bythe cameras 102 of the different vehicles 108. A memory device 130 ofthe system 122 may be similar to the memory device 114 onboard thevehicle 108. For example, the memory device 130 can include one or morecomputer readable storage media that stores the image data obtained byone or more cameras 102 disposed onboard one or more different vehicles108. The image data can be communicated from the vehicles 108 to thememory device 130 at regular intervals (e.g., by wireless communicationor otherwise), on demand by an operator of the vehicle 108, on demand byan operator of the system 122, when the vehicles 108 enter into adesignated area (e.g., a vehicle yard, such as a rail yard), or thelike.

A conversion unit 132 of the system 122 can change the format,appearance, type, or the like, of the image data of the route 106 thatis provided by the vehicle 108. The conversion unit 132 can change astill image and/or video of the route 106 that was obtained by thecamera 102 into a wireframe model or wireframe model data of the route106. Optionally, the conversion unit 132 can modify the image data inother ways.

With continued reference to the examination system 122 shown in FIG. 1,FIG. 2 illustrates an image 200 representative of image data acquired bythe camera 102 onboard the vehicle 108 shown in FIG. 1 according to oneexample of the inventive subject matter described herein. The image 200shows an upcoming segment 206 of the route 106 being traveled upon bythe vehicle 108. The image 200 may be formed from several pixels orother spatial segments (e.g., areas) of the image 200, with the pixelsor other spatial segments having varying image characteristics (e.g.,various colors, intensities, luminance, or the like).

The conversion unit 132 can examine the image characteristics of thepixels or other spatial segments to determine which pixels or spatialsegments having similar image characteristics. With respect to the image200, the conversion unit 132 can determine that the pixels or spatialsegments that represent rails 202, 204 of the route 106 have similarimage characteristics, as well as other portions of the image 200. Thepixels or spatial segments having the same or similar imagecharacteristics (e.g., within a designated range of each other) areassociated with a first group of the pixels or spatial segments of theimage 200. Other pixels or spatial segments having different imagecharacteristics (e.g., outside of the designated range) may not beassociated with the first group. Optionally, these other pixels orspatial segments may be associated with one or more other groups ofpixels or spatial segments based on the image characteristics.

With continued reference to the examination system 122 shown in FIG. 1and the image 200 shown in FIG. 2, FIG. 3 illustrates wireframe modeldata 302 of the image data illustrated as the image 200 in FIG. 2according to one example of the inventive subject matter describedherein. The conversion unit 132 may change the image data into thewireframe model data 302 by identifying one or more edges, surfaces, orlocations in the image data that belong to or represent the sameobjects. For example, the conversion unit 132 can examine the imagecharacteristics (e.g., intensities, colors, luminance, or the like) ofdifferent locations in the image data. The conversion unit 132 may thenidentify which pixels or other locations in the image data have the sameor similar image characteristics. Pixels or other locations in the imagedata may have the same or similar colors, intensities, luminance, or thelike, when the pixels or locations have intensities, colors, luminance,or the like, that are equivalent or within a designated threshold rangeof one another. The conversion unit 132 can group these pixels orlocations with each other because the pixels or locations having thesame or similar image characteristics may represent the same object.Other pixels or other locations having different image characteristicscan be exploded from this group and/or placed into one or more othergroups.

Optionally, pixels or locations in the image data may be placed into thegroup if the pixels or locations are within a threshold distance of eachother. For example, in selecting the pixels for inclusion in the groupof pixels having same or similar image characteristics, the conversionunit 132 may exclude the pixels that are very far from each other in theimage 200 from the group even if the pixels that are located far fromeach other have similar or identical image characteristics. The pixelshaving similar image characteristics but located far from each other inthe image 200 may not represent the same object, surface, edge, or thelike, even though the image characteristics are similar. Optionally, theconversion unit 132 can include the pixels having similar imagecharacteristics but located far from each other if these pixels areconnected by one or more other sets of pixels having the same or similarimage characteristics.

The conversion unit 132 can create the wireframe model data 302 byassigning a first image characteristic to the pixels or other locationsin the group and assigning a different, second image characteristics toother pixels or locations that are not in the group. For example, theconversion unit 132 can modify the image characteristics of the pixelsor create a new data set (e.g., a wireframe model data set) having imagecharacteristics that are assigned based on whether the pixels areincluded in the group of pixels having similar or equivalent imagecharacteristics. In the illustrated example, the pixels or locations inthe group are assigned a white color while other pixels or locations areassigned a black color. Optionally, other colors or other imagecharacteristics may be used. As a result, a wireframe image 302 isgenerated as shown in FIG. 3.

The wireframe image 302 includes white pixels or lines 300 thatrepresent objects in the field of view 104 of the camera 102 (shown inFIG. 1). In the illustrated example, the lines 300 more clearlyrepresent edges of the rails 202, 204 of the route 106 shown in theimage 200. Other lines in the wireframe image 302 represent otherobjects captured in the field of view of the camera 102 that had similarimage characteristics as the rails 202, 204.

In one aspect, the conversion unit 132 combines image data of the route106 obtained at different times into one or more sets of wireframe modeldata. The wireframe image 302 can represent one such set of wireframemodel data. For example, for each of first, second, third, and so on,image data of the route 106 obtained at different times, the conversionunit 132 may create corresponding first, second, third, and so on,wireframe model data of the image data. The conversion unit 132 may thencombine the first, second, third, and so on, wireframe model data intoaggregate wireframe model data. In order to combine the wireframe modeldata, the conversion unit 132 may identify those pixels or otherlocations in the first, second, third, and so on, wireframe model datahaving a designated image characteristic (e.g., the color white, asshown in FIG. 3). If those pixels having the designated imagecharacteristic appear in the same or approximately the same location inat least a threshold number or percentage of the first, second, third,and so on, wireframe model data, then the conversion unit 132 may createthe aggregate wireframe model data to have the designated imagecharacteristic at the same pixels.

Additionally or alternatively, the conversion unit 132 can combineseveral different sets of image data into combined image data, and thencreate the wireframe model data from the combined image data. Forexample, first, second, third, and so on, image data of the route 106can be obtained at different times and then combined by the conversionunit 132. The conversion unit 132 may combine the first, second, third,and so on, image data by calculating or estimating image characteristicsfor different pixels or other locations in the image data that arerepresentative of the image data across the first, second, third, and soon, image data. As one example, the conversion unit 132 can calculate anaverage, median, or the like, of the image characteristic for a pixel,with the values used to calculate the average, median, or the like,obtained from the different image characteristics for that pixel in thefirst, second, third, and so on, image data. This may be repeated forother pixels in the image data to create the combined image data. Thecombined image data may then be used to create the wireframe model data,as described above.

Combining the wireframe model data, or combining the image data intocombined image data and then creating the wireframe model data from thecombined image data, can reduce the impact of visual noise onidentification or prediction of damage to the route 106. For example,image data obtained at different times may result in at least some ofthe image data being acquired when objects are present on the route 106,such as precipitation (e.g., snow, ice, or the like), leaves or othervegetation, or other foreign objects. But, other image data of the samesegment of the route 106 that is obtained at other times may not includethe objects on the route 106. If just the image data obtained when theobjects were on the route 106 is examined to identify or predict damageto the route 106, then these objects may be incorrectly identified bythe system 122 as damage or a trend toward damage. Combining the imagedata and/or combining the wireframe model data based on image dataacquired at different times can lessen the impact of these temporary ortransitory objects on the route 106 when the wireframe model data isexamined to identify or predict damage to the route 106.

For example, the image characteristic of a pixel may be approximatelyconstant for several sets of image data acquired at different times. Oneset of image data may be acquired at a time when snow was on the route106. The presence of the snow may cause the image characteristic of thatpixel to be significantly different from the image characteristic of thepixel in the image data acquired at other times. But, combining theimage data (e.g., by calculating an average or median imagecharacteristic) can result in the image characteristic of the pixel inthe combined image data to be closer to the image characteristics of thepixel in the image data acquired at times other than when snow was onthe route 106 than to the image characteristic of the pixel in the imagedata acquired when snow was on the route 106.

Returning to the description of the system 100 shown in FIG. 1, theexamination system 122 can include an analysis unit 128 that includeshardware circuits or circuitry that include and/or are connected withone or more computer processors, such as one or more computermicroprocessors or other electronic logic-based devices. The analysisunit 128 examines the image data obtained at the different times toidentify changes (e.g., historical trends) in the route 106 as shown inthe image data. For example, the analysis unit 128 can compare differentsets of wireframe model data to determine if the route 106 is changingover time. The different sets of the wireframe model data can representimage data acquired at different times. The analysis unit 128 candetermine if the size, shape, or the like of the route 106 as shown inthe wireframe model data is changing, how the route 106 is changing asreflected in the wireframe model data, or the like. Based on thesechanges, the analysis unit 128 can predict if and/or when the route 106is in need of repair and/or maintenance.

FIG. 4 illustrates wireframe model data 404 representative of the imagedata acquired of the route 106 according to another example of theinventive subject matter described herein. The image data used to createthe wireframe model data 404 shown in FIG. 4 may be image data acquiredlater and/or at different times than the image data used to create thewireframe model data 302 shown in FIG. 3. As shown in the wireframemodel data 302 in FIG. 3, the rails 202, 204 are represented byelongated, substantially parallel or parallel lines 300. But, as shownin the wireframe model data 404, the rail 202 is no longer formed fromsuch elongated lines 300. Instead, the rail 202 is separated intoshorter, discrete segments 400 of approximately parallel lines. Thesesegments 400 are separated by gaps 402. In the illustrated example thereare four (4) segments 400 separated by three (3) gaps 402. These gaps402 may be present because the image data used to create the wireframemodel data 404 does not have pixels or locations with similar or thesame intensities, color, luminance or the like in the locations of thegaps 402 as are in the segments 400. Consequently, when the conversionunit 132 creates the wireframe model data 404, the rail 202 is no longerformed from longer lines 300. Instead, the rail 202 is separated intodiscreet separated segments 400 which are separated by gaps 402.Conversely the rail 204 is still predominantly formed from the longerlines 300 that also appear in the wireframe model data FIG. 3. This mayindicate that the rail 202 has undergone changes over time. The rail204, however, has not undergone these similar changes as the rail 204appears similar in the wireframe model data 302 and the subsequentlyacquired image data used to create the wireframe model data 404.

FIG. 5 illustrates additional wireframe model data 502 representative ofthe image data acquired of the route 106 according to another example ofthe inventive subject matter described herein. The image data used tocreate the wireframe model data 502 may be image data acquired laterand/or at different times than the image data used to create thewireframe model data 302 shown in FIG. 3 and/or the wireframe model data404 shown in FIG. 4.

As shown in FIG. 5, the lines 300 representative of the right rail 204have shifted or moved from previous positions shown in the wireframemodel data 302, 404. For example, the image data used to generate thewireframe model data in FIG. 3 and/or FIG. 4 may be obtained at anearlier point in time or over periods of time that precede when theimage data used to generate the wireframe model data 502 in FIG. 5. Thewireframe model data 502 illustrates the right rail 204 bending orshifting to the right slightly relative to positions of the right rail204 in the wireframe model data 302, 404. For example, the right rail204 shown in FIG. 5 includes a bent or non-linear portion 500 relativeto the lines 300 representative of the right rail 204 shown in FIGS. 3and 4.

Returning to the description of the system 100 shown in FIG. 1, theanalysis unit 128 can examine the wireframe model data and identifychanges in the wireframe model data over time. For example, thewireframe model data of a section of the route 106 may be updatedperiodically, on demand, or at other times. When the wireframe modeldata is updated, the analysis unit 128 can compare the updated wireframemodel data to the previous wireframe model data to determine if thewireframe model data has changed. With respect to a comparison betweenthe wireframe model data 302 and the wireframe model data 404, theanalysis unit 128 can determine that the wireframe model data ischanging in that the lines 300 representative of the left rail 202 arebreaking up into smaller segments 400 separated by gaps 402. Withrespect to a comparison between the wireframe model data 302 and/or 404and the wireframe model data 502, the analysis unit 128 can determinethat the wireframe model data is changing in that the lines 300representative of the right rail 204 are bending at the bent portion500.

The analysis unit 128 can identify a historical trend or changes in thewireframe model data over time and compare this trend to designatedpatterns of damage to the route 106. As one example, the analysis unit128 may count the number of gaps 402 and/or segments 400 that appear ordevelop over time in the wireframe model data. The changes in the numberof segments 400 and/or gaps 402 can represent a historical trend ofchanges in the route 106. In another example, the analysis unit 128 maymeasure the size (e.g., length) of the segments 400 and/or the gaps 402,and monitor changes in the sizes of the segments 400 and/or gaps 402 asa historical trend of changes in the route 106. As another example, theanalysis unit 128 can examine changes in location and/or shapes of thelines 300 representative of the rails 202, 204. For example, theanalysis unit 128 can examine the wireframe model data over time todetermine if the lines 300 move, bend (e.g., become less linear), orotherwise change shape. These changes in the lines 300 can representanother historical trend of changes in the route 106.

The memory device 130 can store different designated changes in thewireframe model data, and these designated changes can be associatedwith different trends of damage to the route 106. The designated changescan be referred to as designated patterns, as the changes representpatterns of change in the route 106 over time. For example, differentnumbers of segments 400 and/or gaps 402 in the wireframe model data maybe associated with different types of damage. Smaller numbers ofsegments 400 and/or gaps 402 may be associated with pitting or othersurface damage to the route 106, which larger numbers of the segments400 and/or gaps 402 may be associated with more severe damage, such asbreaks in the rails 202, 204. As the number of the segments and/or gapsincreases over time, the route may be in more urgent need of repairand/or maintenance.

As another example, smaller distances that the lines representative ofthe rails 202, 204 move or change shape between different sets ofwireframe model data may indicate slight displacement of the rails 202,204, while larger distances that the lines move and/or change shape mayindicate that the rails 202, 204 are severely damaged or misaligned.Increased movement of these lines over time may indicate a more urgentneed of repair and/or maintenance.

The analysis unit 128 can determine the actual changes in the wireframemodel data from the comparisons of the wireframe model data (e.g., theactual historical trend of the route 106) and compare these actualchanges with the designated patterns stored in the memory device 130 (orelsewhere). If the actual changes match one or more designated patterns,then the analysis unit 128 can identify the type and/or severity of thedamage associated with the matching designated pattern as being theactual type and/or severity of the damage to the route 106. For example,the analysis unit 128 can determine that development of a relativelysmall number of segments 400 and/or gaps 402 in the wireframe model datamay more closely match a first pattern (indicative of a small amount ofsurface damage to the route 106) than one or more other patterns (thatindicate more severe damage). As a result, the analysis unit 128 candetermine that the actual changes in the wireframe model data indicate asmall amount of surface damage to the route 106. As another example, theanalysis unit 128 can determine that development of a large gap 402 inthe wireframe model data may more closely match a second pattern(indicative of a break in the route 106) than one or more other patterns(that indicate less severe damage). As a result, the analysis unit 128can determine that the actual changes in the wireframe model dataindicate a break in the route 106. In another example, the analysis unit128 can determine that the movement of the lines 300 in the bent portion500 of the wireframe model data more closely matches a third pattern(indicative of misalignment in the route 106) than one or more otherpatterns (that indicate no misalignment, a lesser amount ofmisalignment, or a greater amount of misalignment). As a result, theanalysis unit 128 can determine that the actual changes in the wireframemodel data indicate some bending in the route 106.

Based on the type and/or severity of the damage to the route 106, theanalysis unit 128 can predict if and/or when repair, maintenance,inspection, or other actions need to be taken with respect to the route106. For example, more severe damage to the route 106 (e.g., a break)may require repair before other degrees of damage to the route 106(e.g., minor corrosion). Similarly, some bending of the route 106 mayrequire inspection, but not urgent inspection, of the route 106. Theanalysis unit 128 can direct the communication unit 126 to communicate arequest signal to one or more locations. This request signal can be sentto direct personnel to repair the route, inspect the route, and/ormaintain the route based on the comparison between the historical trendin changes on the image data with the designated patterns of changes inthe image data. The request signal can inform the recipients of thesignal of the location of the damage to the route 106, the type ofdamage, and/or the severity of the damage to the route 106.

FIG. 6 illustrates a flow chart of a method 600 for analyzing image dataover time of a route to predict when repair and/or maintenance of theroute may be needed according to an example embodiment of the inventivesubject matter described herein. The method 600 may be performed by oneor more embodiments of the systems 100, 122 described herein.

At 602, image data of a route is obtained at different times. Asdescribed above, this can result from different vehicle systems havingcameras disposed onboard obtaining pictures and/or video of the sameportions of the route at different times and over extended periods oftime. Optionally, the image data also may include images and/or videoobtained by stationary wayside devices or other cameras.

At 604, this image data is converted into wireframe model data. Asdescribed above, the image data may be converted into wireframe modeldata by examining image characteristics of the image data over time. Thewireframe model data by can be created by assigning different imagecharacteristics (e.g., colors, intensities, etc.) to different groups ofpixels or other locations in the image data that have the same orsimilar image characteristics.

Optionally, at 606, different sets of wireframe model data can becombined to filter out image data that represents temporary externalfactors. For example, the wireframe model data can be averaged orotherwise combined so that the impact or significance of imagecharacteristics that are based on precipitation, vegetation, or thelike, can be reduced in the wireframe model data relative to the imagecharacteristics that represent the route. Alternatively, the operationsof 606 are not performed.

At 608, the wireframe model data is examined to determine if there is ahistorical trend in changes to the route. For example, the linesrepresentative of rails and/or other surfaces of the route may beexamined and/or compared between wireframe model data representativeimage data acquired at different times. Changes in the lines, such aschanging shapes, locations, sizes, or the like, can indicate degradationof the route.

At 610, a determination is made as to whether the historical trend ofchanges in the wireframe model data indicates damage to the route and/ora need for maintenance. For example, the breaking up of a line in thewireframe model data into a number of shorter segments may be comparedto designated numbers of segments stored in a memory device. Dependingon which one of these designated numbers of segments matches the actualnumber of segments that the line has been broken up in to, the method600 can determine if the route is degrading and/or the severity ofdegradation.

Based on the different types and/or severity and/or damage and/ordegradation to the route, as determined from the changes in thewireframe model data over time, the method 600 may determine how urgentthe need for maintenance and/or repair is. For example, the breaking upof a line in the wireframe model data based on previously acquired imagedata into many more segments may indicate that maintenance and/or repairwill be needed sooner than if the line were not broken up or were brokenup in to fewer segments. Similarly, smaller changes in inter railspacing may reflect a less urgent need for maintenance and/or repair tothe route. But, larger changes in inter rail spacing may reflect a lessurgent need for maintenance and/or repair to the route.

If the changes in the wireframe model data indicate damage to the routeand/or damage that is need of repair and/or maintenance, then flow ofthe method 600 can continue to 612. On the other hand, if there are nochanges, the changes do not indicate worsening damage to the route,and/or the changes do not indicate damage that is need of repair and/ormaintenance, then flow of the method 600 can return to 602.

At 612, the type of maintenance and/or repair that is needed on theroute based on the historical changes in the wireframe model data. Forexample, if the changes in the wireframe model data indicate slightmovements in the route, then the changes may indicate that the routeshould be examined for movement of the rails during the next scheduledinspection of the route, but that no extra inspection needs to beperformed. But, if the changes in the wireframe model data indicatelarger movements in the route, then the changes may indicate that theroute should be examined very soon and, if necessary, prior to the nextscheduled inspection of the route.

At 614, one or more request signals are communicated (e.g.,autonomously, without operator intervention), to request repair and/ormaintenance to the route. For example, depending on how severe thedamage and/or how urgent the repair and/or maintenance is needed to theroute, the method may send an appropriate message to one or morefacilities and/or personnel to inspect, repair and/or maintain theroute.

In another example of the inventive subject matter, a system (e.g., aroute damage prediction system) includes one or more cameras, aconversion unit, and an analysis unit. The cameras are configured toobtain image data within one or more fields of view of the one or morecameras that include a route that is traveled upon by plural differentvehicles. The image data includes at least one of still images or videoof the route obtained at different times. The conversion unit includesone or more computer processors configured to at least one of createwireframe model data or modify the image data into the wireframe modeldata representative of the route. The analysis unit includes one or morecomputer processors configured to examine changes in the wireframe modeldata to identify a historical trend of changes in the image data. Theanalysis unit is configured to compare the historical trend of thechanges in the image data with designated patterns of changes in thewireframe model data to determine when to request at least one ofrepair, inspection, or maintenance of the route. Multiple instances of“one or more processors” does not mean the analysis unit and theconversion unit are embodied in different processors, although that is apossibility. Instead, the one or more processors of the conversion unitmay be the same as the one or more processors of the analysis unit, suchthat in one embodiment the conversion unit and the analysis unit areembodied in the same processor or the same multiple processors.

In one aspect, the system also includes a communication unit configuredto communicate a request signal to direct the at least one of repair,inspection, or maintenance of the route to be performed based oncomparing the historical trend of the changes in the image data with thedesignated patterns.

In one aspect, the conversion unit is configured to create the wireframemodel data from different sets of the image data of the route acquiredat the different times by the different vehicles.

In one aspect, the conversion unit is configured to at least one ofcreate the wireframe model data or modify the image data into thewireframe model data by identifying at least one of pixels or otherlocations in the image data having image characteristics that are withindesignated ranges of each other and assigning a common imagecharacteristic in the wireframe model data to the at least one of pixelsor other locations having the image characteristics that are within thedesignated ranges of each other.

In one aspect, the image characteristics include at least one ofintensities, colors, or luminance.

In one aspect, the analysis unit is configured to filter out changes inthe image data caused by external factors other than damage to theroute, wherein the wireframe model data that is examined by the analysisunit to identify the historical trend includes the wireframe model dataafter filtering out the changes in the image data caused by the externalfactors.

In one aspect, the historical trend of changes in the image dataincludes at least one of changes in a number of lines representative ofthe route in the image data, changes in spacing between segments of thelines in the image data, changes in lengths of the lines or the segmentsof the lines, or changes in gaps between the segments of the lines.

In another example of the inventive subject matter described herein, amethod (e.g., for predicting damage to a route) includes receiving imagedata having one or more fields of view that include a route that istraveled upon by plural different vehicles. The image data includes atleast one of still images or video of the route obtained at differenttimes. The method also includes at least one of creating wireframe modeldata or modifying the image data into the wireframe model datarepresentative of the route, examining changes in the wireframe modeldata to identify a historical trend of changes in the image data, andcomparing the historical trend of the changes in the image data withdesignated patterns of changes in the wireframe model data to determinewhen to request at least one of repair, inspection, or maintenance ofthe route.

In one aspect, the method also includes communicating a request signalto direct the at least one of repair, inspection, or maintenance of theroute to be performed based on comparing the historical trend of thechanges in the image data with the designated patterns.

In one aspect, the wireframe model data is created from different setsof the image data of the route acquired at the different times by thedifferent vehicles.

In one aspect, the wireframe model data is created or the image data ismodified into the wireframe model data by identifying at least one ofpixels or other locations in the image data having image characteristicsthat are within designated ranges of each other and assigning a commonimage characteristic in the wireframe model data to the at least one ofpixels or other locations having the image characteristics that arewithin the designated ranges of each other.

In one aspect, the image characteristics include at least one ofintensities, colors, or luminance.

In one aspect, the method also includes filtering out changes in theimage data caused by external factors other than damage to the route.The wireframe model data that is examined to identify the historicaltrend can include the wireframe model data after filtering out thechanges in the image data caused by the external factors.

In one aspect, the historical trend of changes in the image dataincludes at least one of changes in a number of lines representative ofthe route in the image data, changes in spacing between segments of thelines in the image data, changes in lengths of the lines or the segmentsof the lines, or changes in gaps between the segments of the lines.

In another example of the inventive subject matter described herein, asystem (e.g., a route damage prediction system) includes a conversionunit and an analysis unit. The conversion unit is configured to receiveimage data acquired at different times, the image data representing atleast one of still images or video of a common segment of a routetraveled by vehicles. The conversion unit configured to create wireframemodel data from the image data. The analysis unit is configured toexamine the wireframe model data to identify changes in the wireframemodel data over time. The analysis unit also can be configured toexamine the changes in the wireframe model data to determine when torequest at least one of repair, maintenance, or inspection of the commonsegment of the route.

In one aspect, the conversion unit is configured to examine the imagedata to identify pixels in the image data having image characteristicsthat are within a designated range of each other and to create thewireframe model data by assigning a first designated imagecharacteristic to the pixels having the image characteristics that arewithin the designated range of each other and assigning a different,second designated image characteristic to the pixels having the imagecharacteristics that are not within the designated range of each other.

In one aspect, the image characteristics include at least one of pixelintensities, colors, or luminance.

In one aspect, the conversion unit is configured to create differentsets of the wireframe model data representative of the image data at thedifferent times, and the analysis unit is configured to compare thedifferent sets of the wireframe model data to determine when to requestthe at least one of repair, maintenance, or inspection of the commonsegment of the route.

In one aspect, the vehicles are separate from each other. For example,the vehicles may be mechanically decoupled from each other such that thevehicles can travel on the route at different times, at differentspeeds, in different directions, or the like, relative to each other.The conversion unit can be configured to receive the image data fromcameras disposed onboard the vehicles as the vehicles separately travelon the route at different times.

In one aspect, the analysis unit is configured to compare the changes inthe wireframe model data with designated changes associated with atleast one of different types or different degrees of damage to thecommon segment of the route.

It is to be understood that the above description is intended to beillustrative, and not restrictive. For example, the above-describedembodiments (and/or aspects thereof) may be used in combination witheach other. In addition, many modifications may be made to adapt aparticular situation or material to the teachings of the inventivesubject matter without departing from its scope. While the dimensionsand types of materials described herein are intended to define theparameters of the inventive subject matter, they are by no meanslimiting and are exemplary embodiments. Many other embodiments will beapparent to one of ordinary skill in the art upon reviewing the abovedescription. The scope of the inventive subject matter should,therefore, be determined with reference to the appended claims, alongwith the full scope of equivalents to which such claims are entitled. Inthe appended claims, the terms “including” and “in which” are used asthe plain-English equivalents of the respective terms “comprising” and“wherein.” Moreover, in the following claims, the terms “first,”“second,” and “third,” etc. are used merely as labels, and are notintended to impose numerical requirements on their objects. Further, thelimitations of the following claims are not written inmeans-plus-function format and are not intended to be interpreted basedon 35 U.S.C. §112(f), unless and until such claim limitations expresslyuse the phrase “means for” followed by a statement of function void offurther structure.

This written description uses examples to disclose several embodimentsof the inventive subject matter and also to enable a person of ordinaryskill in the art to practice the embodiments of the inventive subjectmatter, including making and using any devices or systems and performingany incorporated methods. The patentable scope of the inventive subjectmatter may include other examples that occur to those of ordinary skillin the art. Such other examples are intended to be within the scope ofthe claims if they have structural elements that do not differ from theliteral language of the claims, or if they include equivalent structuralelements with insubstantial differences from the literal languages ofthe claims.

The foregoing description of certain embodiments of the inventivesubject matter will be better understood when read in conjunction withthe appended drawings. To the extent that the figures illustratediagrams of the functional blocks of various embodiments, the functionalblocks are not necessarily indicative of the division between hardwarecircuitry. Thus, for example, one or more of the functional blocks (forexample, processors or memories) may be implemented in a single piece ofhardware (for example, a general purpose signal processor,microcontroller, random access memory, hard disk, and the like).Similarly, the programs may be stand-alone programs, may be incorporatedas subroutines in an operating system, may be functions in an installedsoftware package, and the like. The various embodiments are not limitedto the arrangements and instrumentality shown in the drawings.

As used herein, an element or step recited in the singular and proceededwith the word “a” or “an” should be understood as not excluding pluralof said elements or steps, unless such exclusion is explicitly stated.Furthermore, references to “an embodiment” or “one embodiment” of theinventive subject matter are not intended to be interpreted as excludingthe existence of additional embodiments that also incorporate therecited features. Moreover, unless explicitly stated to the contrary,embodiments “comprising,” “including,” or “having” an element or aplurality of elements having a particular property may includeadditional such elements not having that property.

Since certain changes may be made in the above-described systems andmethods without departing from the spirit and scope of the inventivesubject matter herein involved, it is intended that all of the subjectmatter of the above description or shown in the accompanying drawingsshall be interpreted merely as examples illustrating the inventiveconcept herein and shall not be construed as limiting the inventivesubject matter.

What is claimed is:
 1. A system comprising: one or more camerasconfigured to obtain image data within one or more fields of view of theone or more cameras that include a route that is traveled upon by pluraldifferent vehicles, the route including a track having one or morerails, the image data including at least one of still images or video ofthe route obtained at different times; a conversion unit including oneor more computer processors configured to at least one of createwireframe model data based on the image data or modify the image datainto the wireframe model data representative of the route, the wireframemodel data including substantially parallel line segments that representthe one or more rails in the image data, the conversion unit furtherconfigured to filter out a temporary external factor on the one or morerails in the image data at a location of the route, the temporaryexternal factor filtered out by combining at least one of different setsof the image data of the location of the route acquired at the differenttimes or different sets of the wireframe model data of the location ofthe route that are based on the sets of image data to produce combinedwireframe model data, wherein the temporary external factor is otherthan damage to the location of the route and is absent from the one ormore rails in at least one of the sets of the image data; and ananalysis unit including one or more computer processors configured toexamine the line segments of the combined wireframe model data toidentify changes to the line segments over time that represent ahistorical trend of changes to the one or more rails, wherein theanalysis unit is configured to compare the historical trend of thechanges to the one or more rails with designated patterns of changes inthe wireframe model data to determine when to request at least one ofrepair, inspection, or maintenance of the route.
 2. The system of claim1, further comprising a communication unit configured to communicate arequest signal to direct the at least one of repair, inspection, ormaintenance of the route to be performed based on comparing thehistorical trend of the changes to the one or more rails with thedesignated patterns.
 3. The system of claim 1, wherein the conversionunit is configured to at least one of create the wireframe model data ormodify the image data into the wireframe model data by identifying atleast one of pixels or other locations in the image data having imagecharacteristics that are within designated ranges of each other, whereinthe line segments correspond to groups of pixels or locations havingimage characteristics that are within the designated ranges of eachother.
 4. The system of claim 1, wherein the historical trend of changesto the one or more rails develops over time and includes at least one ofchanges in a number of the line segments representative of the one ormore rails in the combined wireframe model data, changes in spacingbetween the line segments in the combined wireframe model data, changesin lengths of the line segments, changes in shapes of the line segments,or changes in gaps between the line segments.
 5. The system of claim 1,wherein the designated patterns are predetermined patterns stored in amemory device, the analysis unit determining when to request at leastone of repair, inspection, or maintenance of the route based on thehistorical trend of the changes to the one or more rails matching one ofthe predetermined patterns or more closely matching the onepredetermined pattern over other predetermined patterns.
 6. The systemof claim 1, wherein the historical trend of changes to the one or morerails develops over time and includes at least one of changes in anumber of the line segments representative of the one or more rails at alocation of the route, decreases in lengths of the line segmentsrepresentative of the one or more rails at the location, or changes ingaps between the line segments representative of the one or more railsat the location.
 7. The system of claim 1, wherein the analysis unit isconfigured to compare the historical trend of the changes to the one ormore rails to the designated patterns by comparing the changes to theline segments that represent the one or more rails to the designatedpatterns.
 8. The system of claim 1, wherein the analysis unit isconfigured to identify the changes to the line segments based on one ormore of the line segments at least one of bending, changing shape, ormoving over time.
 9. The system of claim 1, wherein the conversion unitis configured to combine the different sets of the image data of thelocation of the route to filter out the temporary external factor on theone or more rails by calculating an average or median imagecharacteristic for at least some pixels in the different sets of theimage data.
 10. A method comprising: receiving image data having one ormore fields of view that include a route that is traveled upon by pluraldifferent vehicles, the route including a track having one or morerails, the image data including at least one of still images or video ofthe route obtained at different times; at least one of creatingwireframe model data based on the image data or modifying the image datainto the wireframe model data representative of the route, the at leastone of creating the wireframe model data or modifying the image datainto the wireframe model data performed by one or more computerprocessors, the wireframe model data including substantially parallelline segments that represent the one or more rails in the image data;filtering out a temporary external factor on the one or more rails inthe image data at a location of the route to at least one of create thewireframe model data or modify the image data into the wireframe modeldata, the temporary external factor filtered out by combining at leastone of different sets of the image data of the location of the routeacquired at the different times or different sets of the wireframe modeldata of the location of the route that are based on the sets of imagedata to produce combined wireframe model data, wherein the temporaryexternal factor is other than damage to the location of the route and isabsent from the one or more rails in at least one of the sets of theimage data; examining the line segments of the combined wireframe modeldata to identify changes to the line segments over time that represent ahistorical trend of changes to the one or more rails; and determiningwhen to request at least one of repair, inspection, or maintenance ofthe route based on the historical trend of the changes to the one ormore rails.
 11. The method of claim 10, further comprising communicatinga request signal to direct the at least one of repair, inspection, ormaintenance of the route to be performed based on the historical trendof the changes to the one or more rails.
 12. The method of claim 10,wherein the wireframe model data is created or the image data ismodified into the wireframe model data by identifying at least one ofpixels or other locations in the image data having image characteristicsthat are within designated ranges of each other and assigning a commonimage characteristic in the wireframe model data to the at least one ofpixels or other locations having the image characteristics that arewithin the designated ranges of each other, wherein the at least one ofpixels or locations having the common image characteristic form one ofthe line segments.
 13. The method of claim 10, wherein the historicaltrend of changes to the one or more rails includes at least one ofchanges in a number of the line segments representative of the one ormore rails in the combined wireframe model data, changes in spacingbetween the line segments in the combined wireframe model data, changesin lengths of the line segments, changes in shapes of the line segments,or changes in gaps between the line segments.
 14. The method of claim10, wherein the changes in the line segments of the combined wireframemodel data include at least one of bending, changing shape, or moving ofone or more of the line segments over time.
 15. The method of claim 10,wherein the different sets of the image data of the location of theroute are combined to filter out the temporary external factor on theone or more rails by calculating an average or median imagecharacteristic for at least some pixels in the different sets of theimage data.
 16. A system comprising: one or more computer processorsconfigured to: receive image data acquired at different times, the imagedata representing at least one of still images or video of a commonsection of a route traveled by vehicles, the route including a trackhaving one or more rails; create wireframe model data from the imagedata, the wireframe model data including substantially parallel linesegments that represent the one or more rails in the image data; examinethe line segments of the wireframe model data to identify changes in theline segments of the wireframe model data over time, the changesincluding at least one of bending, changing shape, or moving of one ormore of the line segments; and examine the changes in the line segmentsof the wireframe model data to determine when to request at least one ofrepair, maintenance, or inspection of the common section of the route;wherein the one or more computer processors are also configured tofilter out a temporary external factor on the one or more rails in theimage data at a location of the route, the temporary external factorfiltered out by combining different sets of the image data of thelocation of the route acquired at the different times, wherein thetemporary external factor is other than damage to the location of theroute and is absent from the one or more rails in at least one of thesets of the image data, and wherein the wireframe model data that isexamined to identify changes in the line segments includes the wireframemodel data after filtering out the temporary external factor on the oneor more rails.
 17. The system of claim 16, wherein the one or morecomputer processors are also configured to examine the image data toidentify pixels in the image data having image characteristics that arewithin a designated range of each other and to create the wireframemodel data by assigning a first designated image characteristic to thepixels having the image characteristics that are within the designatedrange of each other and assigning a different, second designated imagecharacteristic to the pixels having the image characteristics that arenot within the designated range of each other, wherein the pixels havingthe first image characteristic correspond to one of the line segments.18. The system of claim 16, wherein the one or more computer processorsare also configured to compare the changes in the wireframe model datawith designated changes associated with at least one of different typesor different degrees of damage to the common section of the route. 19.The system of claim 16, wherein the one or more computer processors areconfigured to combine the different sets of the image data of thelocation of the route to filter out the temporary external factor on theone or more rails by calculating an average or median imagecharacteristic for at least some pixels in the different sets of theimage data.